P-444 Small business experiences with COVID-19 protection measures: A cross-sectional study comparing employer and employee perspectives in Rural New Brunswick.
Bibliographic record
Abstract
Introduction There is a limited understanding of what is known about the implications of recent occupational health and safety (OHS) protection measures on small business management and employees during the coronavirus disease (COVID-19) pandemic. The study examines the different COVID-19 measures that have been used by small businesses in Miramichi, New Brunswick. Objectives The study identifies the most common OHS protection measures in use within small businesses during COVID-19 and explores whether differences exist in perspectives of employees and managers of small businesses on the most effective OHS protection measures used. Methods Recruitment was collected through convenience sampling between February 6th, 2021 and March 9th, 2021. Participants for the online survey included business management personnel and employees from Miramichi, NB. The cross-sectional study used a web-based survey containing 25 items concerning demographics (n=7), experiences working during COVID-19 (n=7), and information and experiences with characteristics of personal protective equipment (PPE) used (n=11). Results Results showed moderate ratings of positive endorsement (60%) from both employers and employees on the use of COVID-19 OHS protection measures. No significant differences were found between employer and employee perceptions on the effectiveness of employed protection measures. The most frequently used protection measures utilized in these small businesses constitute the three lowest levels of control represented on the NIOSH Hierarchy of Controls: engineering controls, administrative controls, and PPE. Conclusion This study provides new knowledge through the collection of stakeholder perspectives about how current workplace strategies to prevent the spread of COVID-19 in small businesses and may help guide future recommendations for small businesses dealing with other OHS and public health crises.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".